This invention relates to a neural network or net which is trainable to correctly recognize input patterns subjected to disturbances, such as translation and rotation.
Various pattern recognition devices are already in commercial use. As a result of recent research and development, neural networks are used in pattern recognition. For example, such neural networks are disclosed in U.S. Pat. No. 4,975,961 issued to Hiroaki Sakoe and assigned to the present assignee and in U.S. patent application Ser. No. 596,613 filed Oct. 10, 1990 non abandoned, by Hiroaki Sakoe and assigned to the present assignee as a continuation-in-part application based on Japanese Patent Application No. 298,282 of 1987.
In general, a pattern recognition device comprises a category memory for storing categories into which reference patterns are classified. An adaptive input unit preprocesses or adaptively processes input patterns subjected to disturbances into processed patterns. While preprocessing the input patterns, the input unit removes the disturbances from the input patterns to provide rectified patterns as the processed patterns. A recognition unit is connected to the category memory and the input unit for using the processed patterns in recognizing the input patterns as belonging to at least one of the categories by extracting characteristic features from the processed patterns and in producing a recognition result representative of such at least one of the categories.
In the above-referenced Sakoe patent and patent application, the neural network is of the type of a pattern associator neural network described in a book by E. D. Rumelhart and others, "Parallel Distributed Processing" Volume I (MIT Press, 1986), and is trainable or capable of learning in accordance with an algorithm which is known in the art as a back-propagation training algorithm or simply as back propagation art and is disclosed revealed in an article contributed by E. D. Rumelhart and two others to Nature, Volume 323, pages 533 to 536 (Oct. 9, 1986), under the title of "Learning Representation by Back-Propagating Errors". In addition to the pattern associator neural network, a pattern recognition device must comprise several other units and is therefore referred to herein as a neural network assembly. It is more appropriate in such a neural network assembly to refer to the memory as a memory region, the adaptive input unit as an adaptive input region, and the recognition unit as a recognition region.
When using the neural network assembly in correctly and precisely recognizing input patterns subjected to disturbances, the adapitive input region will produce most appropriately processed patterns. It is, however, very difficult to preliminarily determine The manner of removing the disturbances and of extracting the characteristic features in connection with all possible input patterns.